Providing a response that is relevant to a user's search phrase can be challenging. Part of the reason for this is that people don't always express ideas in the same way or desire the same depth of detail in a response. For instance, a person with significant experience in finance will likely express a search phrase directed to learning about interest rates differently than someone with little or no finance experience, and will likely desire a response with a different level of detail as well.
Traditional information retrieval techniques, which typically search and retrieve/identify voluminous amounts of information, are not optimal for providing users with appropriate and relevant responses. This is because these techniques typically rely on vocabulary term matching when searching through documents to retrieve/identify for a response. As such, relevant documents that do not have matching vocabulary words and/or phrases are often not retrieved while documents that are not relevant to the user, but that have matching vocabulary words and/or phrases, are.
Furthermore, knowledge bases that are specifically designed to get users appropriate and relevant responses typically require a great deal of human effort with respect to their maintenance and improvement. As such, tools facilitating this maintenance and improvement can be of great value.